Education & Training Services - Further your AI knowledge by signing up to our of our instructor-led courses

Education & Training Services

Further your AI knowledge by signing up to our instructor-led courses

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As the UK''s leading AI provider, Scan AI is also certified to deliver vendor-certified education and training courses. We offer NVIDIA Deep Learning Institute (DLI) courses and NVIDIA Ideation Workshops, alongside a range of Software Webinars aimed at demonstrating the benefits of selected AI applications. Click the tabs below to explore each further.

NVIDIA Deep Learning Institute Logo

NVIDIA Deep Learning Institute

The NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing. Our DLI courses are delivered by qualified instructors who are in the perfect position to pass on their knowledge and educate developers on how to get the most from this rapidly evolving field. The DLI also teaches you how to optimise your code for performance using NVIDIA, CUDA and OpenACC.

schoolLearn

• Learn from technical industry experts and instructors

• Gain hands-on experience with the most widely used, industry-standard software, tools, and frameworks

editor_choiceQualify

• Earn an NVIDIA DLI certificate in select courses to demonstrate subject matter competency and support professional career growth

desktop_cloud_stackImplement

• Access GPU-accelerated servers in the cloud to complete hands-on exercises

• Build production-quality solutions with the same DLI base environment containers used in the courses, available from the NVIDIA NGC catalogue

Learning that makes an impact

What our attendees say

“I have previously worked with pre-trained AI models but have not attempted or learned about fine-tuning. The workshop was an excellent introduction to the principles behind fine-tuning with an excellent real world scenario for illustration. All in all, I thought the workshop was very well put together and presented.”

Course: Adding New Knowledge to LLMs

“I had a great time on NVIDIA's adding new knowledge to LLMs course, learning all about a process and pipeline that would otherwise require significant investment of both time and money to replicate. This was a fast-paced high-level course covering modifying open-weight LLMs to best suit a specific use case. Unlike many similar courses this dove into detailed retraining and not just prompt engineering. This knowledge is highly applicable to many businesses and we will be directly deploying both the decision-making and practical skills in our work with our future clients.”

Course: Adding New Knowledge to LLMs

“Yet another great course. Full day, hands on, starting from the basics of encoders, transformers, decoders then all the way to agentic workflows, gave me the full picture and new ideas about how I can apply LLMs in my projects. Big thanks to SCAN team for organizing this.

This one, being my second course, actually inspired me even more to have a deeper dive, as far as starting to look into Karpathy's nanochat. Also on the practical side, gave me more confidence to use langchain through the great practical examples used in notebooks.”

Course: Rapid Application Development Using Large Language Models

“I recently attended the NVIDIA Deep Learning Institute course, "Adding New Knowledge to LLMs." As this was my first experience working with and running Large Language Models using NVIDIA tools, I found the course very interesting and informative.

The hands-on exercises helped me understand the practical aspects of LLMs, and the explanations were clear and easy to follow. The course has given me a better understanding of how LLMs can be adapted and applied in real-world scenarios.

Overall, it was a valuable learning experience, and I look forward to exploring this area further in my teaching and research.”

Course: Adding New Knowledge to LLMs

Building RAG Agents with LLMs

eventNext Course Date: 7th December 2026
Previous CourseNext CourseBOOK THIS COURSE
Workshop topics
  • Introduction to the workshop and setting up the environment.
  • Exploration of LLM inference interfaces and microservices.
  • Designing LLM pipelines using LangChain, Gradio, and LangServe.
  • Managing dialogue states and integrating knowledge extraction.
  • Strategies for working with long-form documents.
  • Utilising embeddings for semantic similarity and guardrailing.
  • Implementing vector stores for efficient document retrieval.
  • Evaluation, assessment, and certification.
Course Prerequisites Introductory deep learning knowledge, with comfort with PyTorch and transfer learning preferred. Intermediate Python experience, including object-oriented programming and libraries.

Building Agentic AI Application with LLMs

eventNext Course Date: 25th January 2027
Previous CourseNext CourseBOOK THIS COURSE
1. Fundamentals of Agent Abstraction and LLMs
  • Discuss LLM capabilities and pitfalls.
  • Introduce agents as a task decomposition abstraction.
  • Demonstrate a minimal agent with free-text LLM calls.
2. Structured Output and Basic Fulfillment Mechanisms
  • Use JSON and task-based outputs.
  • Ensure domain alignment and stable schema enforcement.
  • Introduce cognitive architectures.
3. Retrieval Mechanisms and Environmental Tooling
  • Develop tool interfaces for databases and APIs.
  • Use vector-based retrieval-augmented generation over document sets.
4. Multi-Agent Systems and Frameworks
  • Decompose tasks among specialised agents.
  • Formalise communication buffers and process distribution.
  • Compare different agent frameworks.
5. Final Assessment Deploy an agent that schedules multiple retrieval operations to gather research and return it to the user.
Course Prerequisites
  • Introductory deep learning knowledge, including attention mechanisms and transformers.
  • Intermediate Python proficiency and familiarity with ML libraries.

Building AI Agents with Multimodal Models

eventNext Course Date: 8th February 2027
Previous CourseNext CourseBOOK THIS COURSE
About this Course Just like how humans have multiple senses to perceive the world around them, computers have a variety of sensors to help perceive the human world. In the health industry, computed tomography (CT) scans provide a 3D representation used to detect potentially dangerous abnormalities. In the robotics industry, lidars are used to help robots see depth and navigate the complex topology around them. In this course, learners will develop neural network based multimodal models that can understand many different data types by exploring different fusion techniques.
Learning Objectives
  • Different data types and how to make them neural network ready.
  • Model fusion and the differences between early, late, and intermediate fusion.
  • PDF extraction using OCR.
  • The difference between modality and agent orchestration.
  • Customization of NVIDIA AI Blueprints with Video Search and Summarization (VSS).
Topics Covered We'll begin with a robotics use case to show how different datatypes impact an effective neural-networks architecture. The mathematical concepts we learn in the robotics use case can then be applied to Large Language Models (LLMs) in order to modify these powerful models to accept non-language data input. We'll end with orchestration where multiple models work together to answer user queries.
1. Early and Late Fusion (1 hr)
  • Use camera and LiDAR data to predict object positions.
  • Convert various datatypes to make them neural network ready.
2. Intermediate Fusion (1 hr)
  • Explore the theory behind effective multimodal model architecture.
  • Train a Contrastive Pretraining model.
  • Create a vector database.
3. Cross-modal Projection (2 hr)
  • Convert a Language Model into a Vision Language Model (VLM).
  • Process PDFs with Optical Character Recognition (OCR) tools.
4. Model Orchestration (2 hr)
  • Analyze video using Cosmos Nemotron.
  • Use VSS to answer user queries about video content.
  • Orchestrate with NVIDIA AI Blueprints.
5. Assessment (1 hr) Convert a pre-trained model to input a different datatype using projection.
Course Prerequisites
  • A basic understanding of Deep Learning concepts.
  • Familiarity with a Deep Learning framework such as TensorFlow, PyTorch, or Keras. This course uses PyTorch.
  • Tools, libraries, and frameworks used: PyTorch and CLIP.

NVIDIA Isaac for Accelerated Robotics

eventNext Course Date: 10th March 2027
Previous CourseNext CourseBOOK THIS COURSE
Welcome (15 min)
  • Meet the instructor.
  • Set up the environment.
OpenUSD for Robotics (1 hr 45 min)
  • Building blocks of robots in OpenUSD.
  • Importing robots to OpenUSD.
  • Assembling robots and environments.
Software-in-the-Loop Testing for Robotics (2 hr)
  • Integrate ROS with Isaac Sim.
  • Configure ROS graphs, occupancy maps, Nav2, and MoveIt2.
Break (30-60 min)
Accelerating ROS 2 Workloads (1 hr 15 min)
  • Overview of Isaac ROS.
  • Grasp authoring, pick and place simulation.
Synthetic Data Generation (1 hr 45 min)
  • Explore Physical AI and simulation.
  • Collect data with GR00T-Teleop and augment it with GR00T-Mimic.
  • Train robots using Isaac Lab and explore Cosmos WFMs.
Final Review (30 min)
  • Review key learnings, complete the assessment, and take the workshop survey.
Course Prerequisites
  • Experience in Python programming and ROS 2.
  • Familiarity with 3D workflows or CAD tools.
  • Exposure to simulation and robotics workflows.
nvidia software webinar

AI Software Webinars

Software applications are a critical part of any AI workflow, from libraries and frameworks in data preparation and model development through to GPU virtualisation during training and computer vision and orchestration. Our range of webinars aim to show you first-hand how AI-specific applications can revolutionise your productivity, visualisation or time to results.

desktop_windowsRun:ai

Run:ai enables you to maximise GPU utilisation by pooling disparate compute resource and enabling intelligent scheduling and allocation.

analyticsWeights & Biases

W&B helps manage your AI workflows end-to-end by quickly tracking experiments and iterations, evaluating model performance, reproducing models.

identity_platformUbiOps

The UbiOps platform helps teams to quickly run their AI workloads as reliable and secure micro-services, without upending their existing workflows.

task_altSupervisely

Supervisely helps you develop AI faster and better with on-premise, enterprise-grade solutions for every task - from labelling to building production models.

display_settingsYellowdog

Yellowdog provides a single interface to control any compute device - on-prem, hybrid or multi-cloud - supporting any operating system.

Run:ai

BOOK THIS WEBINAR
What is Run:ai? Run:ai software is a scheduling and orchestration platform, that creates virtual 'pools' of GPU resource, so they can be dynamically allocated as tasks require.
Why do I need Run:ai? Run:ai's platform revolutionises AI and machine learning operations by addressing key infrastructure challenges through dynamic GPU resource allocation, comprehensive AI lifecycle support, and strategic resource management. By pooling GPU resources across environments and utilising advanced orchestration and accelerators, Run:ai significantly enhances GPU efficiency and workload capacity. This results in significant increases in GPU availability, workloads, and GPU utilisation, all with zero manual resource intervention, accelerating innovation and providing a scalable, agile, and cost-effective solution for enterprises.
What will I learn on this webinar?

Our webinars are led by one of our in-house data scientists, who will show you how to:

Set up batch scheduling of your workloads

  • Reduce GPU idleness and increase cluster utilisation with job queueing and opportunistic batch job scheduling

Ensure equity amongst workgroups

  • Prevent resource contention with over quota priorities and automatic job preemption and fairshare resource allocation

Get the most from the user-friendly interface

  • Real-time and historical metrics by job, workload, and team in a single dashboard. Assign compute guarantees to critical workloads, promote oversubscription, and react to business needs easily.
nvidia scan ai workshop

NVIDIA AI Ideation Workshop

In collaboration with NVIDIA, the Scan AI team is able to provide an Ideation virtual workshop for your organisation. In this workshop, you will be able to evaluate your existing AI projects and wider strategy, or use the day to formulate an AI plan from scratch. This will be done in collaboration with experts in AI and deep learning practices from both Scan AI and NVIDIA. Following the workshop you will receive a written report with recommendations and guidance as how to implement your plans.

Your workshop will be completely subsidised by Scan and NVIDIA - with no charges and no obligation to purchase anything. Our goal is to promote the wider use of AI technology and show you the possibilities within your industry vertical.

searchInvestigation

• Involve all stakeholders to establish where you are in your AI journey, what are your goals and use cases

• Explore how to accelerate the business, reduce time to insight, and achieve ROI

descriptionPlanning

• Map your goals to a schedule of activities and set priorities

• Create a roadmap to start using AI in your business

desktop_cloud_stackImplementation

• Run pilot projects to gain momentum

• How to build an in-house AI team and provide in-house AI training

• Managing internal and external communications

Example AI Ideation Workshop Agenda

BOOK A WORKSHOP
09:00 - 09:15 Introductions
09:15 - 09:30 Workshop Overview - Confirmation of goals
09:30 - 10:00 AI/Data Science Current State - what has been done, what worked, what didn't.
10:00 - 10:30 AI/Data Science Future State - 1, 3 and 5 year desired state
10:30 - 10:45 Break
10:45 - 12:30 Use Case exploration - most applicable with ROI
12:30 - 13:00 Lunch
13:00 - 14:00 Data Exploration - What data sources are available, how ready for AI?
14:00 - 15:00 Architecture Exploration - what is current and planned architecture for AI?
15:00 - 15:15 Break
15:15 - 16:00 Summary and Initial feedback, indication of AI readiness scale 1-10